Based on the temporal-spatial stochastic radiation field, microwave staring correlated imaging (MSCI) can achieve high resolution reconstruction of the target. When MCSI is applied in remote sensing, the imaging area will be very large. With the increasing of imaging equation scale, the ill-conditioned problem will seriously influence the resolution of imaging. This paper focuses on the algorithm to realize high-resolution MSCI in condition of large imaging area. Inspired by multigrid (MG) method, we use multigrid (MG) method to decrease the low-frequency error and highfrequency error of the solution by dealing with the imaging equation in different scales. Furthermore, we apply Least Square QR-factorization (LSQR) algorithm to solve the large scale ill-conditioned equation. We name the new algorithm as MG-LSQR. Simulation results show that the proposed algorithm could increase the resolution efficiently in condition of large imaging area.
Radar correlated imaging (RCI) is a novel modality to obtain high resolution target images by correlated process of stochastic radiation field and the received signals. Conventional RCI methods neglect the inherent structure information of complex extended target, which makes the quality of recovery result degraded. Thus a clustered sparse Bayesian learning with Laplace prior (La-CSBL) algorithm for extended target imaging is proposed in this paper. A hierarchical correlated Laplace prior model is introduced to consider both the sparse prior and the cluster prior, and the prior for each coefficient not only involves its own hyperparameter, but also its immediate neighbor hyperparameters. Then the algorithm alternates between steps of target reconstruction and parameter optimization by cyclic minimization method under the Bayesian maximum a posteriori framework. Experimental results show that the proposed algorithm could realize high resolution imaging efficiently for extended target.
Radar correlated imaging (RCI) introduces the optical correlated imaging technology to traditional microwave imaging, which has raised widespread concern recently. Conventional RCI methods neglect the structural information of complex extended target, which makes the quality of recovery result not really perfect, thus a novel combination of negative exponential restraint and total variation (NER-TV) algorithm for extended target imaging is proposed in this paper. The sparsity is measured by a sequential order one negative exponential function, then the 2D total variation technique is introduced to design a novel optimization problem for extended target imaging. And the proven alternating direction method of multipliers is applied to solve the new problem. Experimental results show that the proposed algorithm could realize high resolution imaging efficiently for extended target.